Towards Automated Counter-Melody Generation for Monophonic Melodies
Algorithmic composition has focused on creating music from algorithms, stemming from the capacity to convert notes into numbers and vice versa, thus allowing simple to complex algorithmic manipulations. The focus of these studies has either been the creation of melodies, chords, accompaniments or entire songs. This study focuses on a relatively underexplored topic on the algorithmic generation of a counter-melody from a given melody. Using a method based on existing knowledge of generating chords and music theory on compatible notes and chord progressions, combined with concepts of machine learning and tree traversal techniques for generating chords, this study was able to generate 200 counter-melodies from 100 inputs, involving two generation techniques per input. The results show that counter-melodies were successfully generated based on chord progression generation and note selection approaches, and after subjecting the counter-melodies to proper subject evaluation, the average scores of 2.89 and 3.02 on a 5-point evaluation criteria reveal that the counter-melodies are musically fit for the original melodies they were based from.
Prudente, L., & Coronel, A. (2017). Towards automated counter-melody generation for monophonic melodies. Proceedings of the 2017 International Conference on Machine Learning and Soft Computing, 197–202. https://doi.org/10.1145/3036290.3036295